Recognizing Mimicked Autistic Self-Stimulatory Behaviors Using HMMs

Authors:
Tracy Westeyn;Kristin Vadas;Xuehai Bian;Thad Starner;Gregory D. Abowd
Affiliations:
College of Computing and GVU Center/ Georgia Institute of Technology Atlanta, GA 30332-0280 USA;College of Computing and GVU Center/ Georgia Institute of Technology Atlanta, GA 30332-0280 USA;College of Computing and GVU Center/ Georgia Institute of Technology Atlanta, GA 30332-0280 USA;College of Computing and GVU Center/ Georgia Institute of Technology Atlanta, GA 30332-0280 USA;College of Computing and GVU Center/ Georgia Institute of Technology Atlanta, GA 30332-0280 USA
Venue:
ISWC '05 Proceedings of the Ninth IEEE International Symposium on Wearable Computers
Year:
2005

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Abstract

Children with autism often exhibit self-stimulatory (or "stimming") behaviors. We present an on-body sensing system for continuous recognition of stimming activity. By creating a system to recognize and monitor stimming behaviors, we hope to provide autism researchers with detailed, quantitative data. In this paper, we compare isolated and continuous recognition rates of emulated autistic stimming behaviors using hidden Markov models (HMMs). We achieved an overall system accuracy 68.57% in continuous recognition tests. However, the occurrence of stimming events can be detected with 100% accuracy by allowing minor frame-level insertion errors.